TAILIEUCHUNG - Classification-Aware Hidden-Web Text Database Selection

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Inclusive financial systems—allowing broad access to financial services, with- out price or nonprice barriers to their use—are especially likely to benefit poor people and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or become entrepreneurs—and small enterprises must rely on their limited earn- ings to pursue promising growth opportunities. This can contribute to persistent income inequality and slower economic growth. . | 6 Classification-Aware Hidden-Web Text Database Selection PANAGIOTIS G. IPEIROTIS New York University and LUIS GRAVANO Columbia University Many valuable text databases on the web have noncrawlable contents that are hidden behind search interfaces. Metasearchers are helpful tools for searching over multiple such hidden-web text databases at once through a unified query interface. An important step in the metasearching process is database selection or determining which databases are the most relevant for a given user query. The state-of-the-art database selection techniques rely on statistical summaries of the database contents generally including the database vocabulary and associated word frequencies. Unfortunately hidden-web text databases typically do not export such summaries so previous research has developed algorithms for constructing approximate content summaries from document samples extracted from the databases via querying. We present a novel focused-probing sampling algorithm that detects the topics covered in a database and adaptively extracts documents that are representative of the topic coverage of the database. Our algorithm is the first to construct content summaries that include the frequencies of the words in the database. Unfortunately Zipf s law practically guarantees that for any relatively large database content summaries built from moderately sized document samples will fail to cover many low-frequency words in turn incomplete content summaries might negatively affect the database selection process especially for short queries with infrequent words. To enhance the sparse document samples and improve the database selection decisions we exploit the fact that topically similar databases tend to have similar vocabularies so samples extracted from databases with a similar topical focus can complement each other. We have developed two database selection algorithms that exploit this observation. The first algorithm proceeds hierarchically and .

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